Caching Natural Language Questions and Results in a Question and Answer System

ABSTRACT

Mechanisms are provided for answering an input question. An input question to be answered from a source is received and processed to one or more extract features of the input question. The extracted one or more features are compared to cached features stored in one or more entries of a question and answer (QA) cache. A determination is made as to whether there is a matching entry in the one or more entries of the QA cache based on results of the comparing and, if so, candidate answer information is retrieved from the matching entry. The retrieved candidate answer information is returned to the source of the input question as candidate answer information for answering the input question.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for cachingnatural language questions and results in a question and answer (QA)system.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems which may take an input question, analyze it, and return resultsindicative of the most probable answer to the input question. QA systemsprovide automated mechanisms for searching through large sets of sourcesof content, e.g., electronic documents, and analyze them with regard toan input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of open domain question answering. The Watson™system is built on IBM's DeepQA™ technology used for hypothesisgeneration, massive evidence gathering, analysis, and scoring. DeepQA™takes an input question, analyzes it, decomposes the question intoconstituent parts, generates one or more hypothesis based on thedecomposed question and results of a primary search of answer sources,performs hypothesis and evidence scoring based on a retrieval ofevidence from evidence sources, performs synthesis of the one or morehypothesis, and based on trained models, performs a final merging andranking to output an answer to the input question along with aconfidence measure.

Various United States Patent Application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing systemcomprising a processor and a memory, for answering an input question.The method comprises receiving, in the data processing system, an inputquestion to be answered from a source and processing, by the dataprocessing system, the input question to extract one or more features ofthe input question. The method further comprises comparing, by the dataprocessing system, the extracted one or more features to cached featuresstored in one or more entries of a question and answer (QA) cache of thedata processing system. Moreover, the method comprises determining, bythe data processing system, whether there is a matching entry in the oneor more entries of the QA cache based on results of the comparing andretrieving, by the data processing system, in response to a matchingentry being present in the one or more entries of the QA cache,candidate answer information from the matching entry. In addition, themethod comprises returning, by the data processing system, the retrievedcandidate answer information to the source of the input question ascandidate answer information for answering the input question.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 is an example diagram of a distributed data processing system inwhich aspects of a Question and Answer system of the illustrativeembodiments may be implemented;

FIG. 2 is an example block diagram of a computing device in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment; and

FIG. 4 is a flowchart outlining an example operation of a QA systempipeline implementing caching logic in accordance with one illustrativeembodiment.

DETAILED DESCRIPTION

Question and Answer (QA) systems, such as IBM's Watson™ QA system,require a significant amount of compute power to analyze a naturallanguage question and determine the results from candidate findings.Thus, it is desirable to reduce the required amount of compute powerwhenever possible. The illustrative embodiments provide mechanisms tocache natural language questions and their corresponding answers toreduce the computational time/resources required to provide a responseto a submitted question. In instances where a submitted question issignificantly similar to a previously processed question, much of the QAsystem pipeline processing can be circumvented and answers previouslygenerated for a previously processed similar question may be used. Inthis way, response time is increased and computational time/resourcesare significantly reduced.

In general, methods for caching search queries for full text indices,relational databases, and other types of data stores are known. However,these known mechanisms are generally scoped to retrieval of data from aprimary store based on a cache of normalized terms or query componentsand/or changes within the temporal aspects of the data. Contrary tothese known mechanisms, the illustrative embodiments of the presentinvention provide mechanisms for caching questions and answers andproviding a mechanism for comparing currently received questions withpreviously cached questions to determine similarities and identifypotential answers for the currently received questions based onpreviously submitted similar questions and the answers that weregenerated for these previously submitted similar questions.

With the mechanisms of the illustrative embodiments, a cache store isprovided for storing information about previously processed questionsand the candidate answers returned by a QA system as part of theprocessing of these questions. Moreover, logic is provided to determinea similarity between a current question being processed and one or moreof the cached previously processed questions. If a sufficiently similarcached previously processed question is identified, then thecorresponding cached candidate answers may be retrieved and used togenerate a set of candidate answers for the question currently beingprocessed. As a result, the lengthy and resource intensive naturallanguage processing of the question and the candidate answers by a QAsystem may be avoided by returning the cached candidate answers as thecandidate answers for the currently processed question.

During question processing in a QA system, the question undergoes acomplex analysis comprising many multi-step processes such as parsing,part-of-speech tagging, named entity detection, and other featureextraction processes, annotation, and the like. Once the particularquestion is answered through the generation of a set of candidateanswers and a final answer for the input question, the results of theanalysis, e.g., the extracted features, named entities, part of speechtag information, and the like, are lost and would have to be re-createdeven if the exact same question is submitted again. That is, thecomplex, lengthy, and resource intensive analysis would have to berepeated due to the loss of the intermediate analysis resultinformation.

With the mechanisms of the illustrative embodiments, the intermediatenatural language processing result information is persisted in a cachestore in association with the ultimate candidate answer result(s)returned by the QA system to the submitter of the input question. Thepersisted intermediate natural language processing result information isused as a basis for determining a similarity between a newly submittedquestion and the previously processed question that caused theintermediate natural language processing result information to begenerated and persisted, hereafter referred to as the previouslyprocessed question. When a new question is received for processing bythe QA system, the new question is parsed and partially processed toextract a minimized preliminary set of features sufficient enough toidentify a similar previously processed question in the cache store, ifone exists.

Logic is provided for identifying a previously processed question in thecache store that is the most similar to the newly received question.Thresholds may be provided for identifying a minimum level of similarityrequired to identify a sufficiently similar previously processedquestion in the cache store. If a previously processed question isidentified in the cache store that has the minimum level of similarity,the most similar previously processed question is selected and itscorresponding candidate answer(s), or final answer, is returned as thecandidate answer(s) or final answer for the newly received question,thereby eliminating the need to re-execute the entire QA system pipelineanalysis. New questions for which a sufficiently similar matchingpreviously processed question in the cache store cannot be found willundergo the standard QA system pipeline analysis with its informationbeing cached in the cache store for use in handling the processing offuture questions. If previously processed questions in the cache storeare identified that do not have the requisite minimum level ofsimilarity, but may be within a particular range of this minimum levelof similarity, such as may be defined by one or more thresholds, then alisting of these previously processed questions may be returned for userselection of a correct candidate answer for the newly submittedquestion. This information may likewise be fed back to the cache logicto update the cache store with information indicating the answerselected by a user as a correct answer for the cached previouslyprocessed question.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method, or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in any one or more computer readablemedium(s) having computer usable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be a system, apparatus, or device of an electronic,magnetic, optical, electromagnetic, or semiconductor nature, anysuitable combination of the foregoing, or equivalents thereof. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical device havinga storage capability, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiberbased device, a portable compact disc read-only memory (CDROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any tangible medium that cancontain or store a program for use by, or in connection with, aninstruction execution system, apparatus, or device.

In some illustrative embodiments, the computer readable medium is anon-transitory computer readable medium. A non-transitory computerreadable medium is any medium that is not a disembodied signal orpropagation wave, i.e. pure signal or propagation wave per se. Anon-transitory computer readable medium may utilize signals andpropagation waves, but is not the signal or propagation wave itself.Thus, for example, various forms of memory devices, and other types ofsystems, devices, or apparatus, that utilize signals in any way, suchas, for example, to maintain their state, may be considered to benon-transitory computer readable media within the scope of the presentdescription.

A computer readable signal medium, on the other hand, may include apropagated data signal with computer readable program code embodiedtherein, for example, in a baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, including, but notlimited to, electro-magnetic, optical, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that cancommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Similarly, a computer readable storage medium is any computer readablemedium that is not a computer readable signal medium.

Computer code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination thereof.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java™, Smalltalk™, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer, or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to the illustrativeembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions thatimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The illustrative embodiments may be utilized in many different types ofdata processing environments. FIGS. 1-3 are directed to describing anexample Question/Answer, Question and Answer, or Question Answering (QA)system, methodology, and computer program product with which themechanisms of the illustrative embodiments may be implemented. As willbe discussed in greater detail hereafter, the illustrative embodimentsmay be integrated in, and may augment and extend the functionality of,these QA mechanisms with regard to caching intermediate questionprocessing information and resulting corresponding candidate answerinformation for purposes of processing subsequent similar questions.FIGS. 1-3 are intended to be only examples of data processingenvironments in which the aspects of the various illustrativeembodiments may be implemented. Many modifications may be made to thedepicted example environments without departing from the spirit andscope of the present invention.

Since the illustrative embodiments augment and extend the functionalityof QA systems, it is important to first have an understanding of howquestion and answer creation in a QA system may be implemented beforedescribing how the mechanisms of the illustrative embodiments areintegrated in and augment such QA systems. It should be appreciated thatthe QA mechanisms described in FIGS. 1-3 are only examples and are notintended to state or imply any limitation with regard to the type of QAmechanisms with which the illustrative embodiments may be implemented.Many modifications to the example QA system shown in FIGS. 1-3 may beimplemented in various embodiments of the present invention withoutdeparting from the spirit and scope of the present invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, maydetermine use cases for products, solutions, and services described insuch content before writing their content. Consequently, the contentcreators may know what questions the content is intended to answer in aparticular topic addressed by the content. Categorizing the questions,such as in terms of roles, type of information, tasks, or the like,associated with the question, in each document of a corpus of data mayallow the QA system to more quickly and efficiently identify documentscontaining content related to a specific query. The content may alsoanswer other questions that the content creator did not contemplate thatmay be useful to content users. The questions and answers may beverified by the content creator to be contained in the content for agiven document. These capabilities contribute to improved accuracy,system performance, machine learning, and confidence of the QA system.Content creators, automated tools, or the like, may annotate orotherwise generate metadata for providing information useable by the QAsystem to identify these question and answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to reduce thecomputation time and resource cost for subsequent processing ofquestions that are similar to questions already processed by the QAsystem.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or more computing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 may include multiple computing devices 104in communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. The QA system 100 andnetwork 102 may enable question/answer (QA) generation functionality forone or more QA system users via their respective computing devices110-112. Other embodiments of the QA system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources, where the term “pipeline”refers to a series of stages of analysis/execution, performed by one ormore sets of logic, modules, engines, or the like, in the QA system 100.For example, the QA system 100 may receive input from the network 102, acorpus of electronic documents 106, QA system users, or other data andother possible sources of input. In one embodiment, some or all of theinputs to the QA system 100 may be routed through the network 102. Thevarious computing devices 104 on the network 102 may include accesspoints for content creators and QA system users. Some of the computingdevices 104 may include devices for a database storing the corpus ofdata 106 (which is shown as a separate entity in FIG. 1 for illustrativepurposes only). Portions of the corpus of data 106 may also be providedon one or more other network attached storage devices, in one or moredatabases, or other computing devices not explicitly shown in FIG. 1.The network 102 may include local network connections and remoteconnections in various embodiments, such that the QA system 100 mayoperate in environments of any size, including local and global, e.g.,the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document may include any file, text, article, or sourceof data for use in the QA system 100. QA system users may access the QAsystem 100 via a network connection or an Internet connection to thenetwork 102, and may input questions to the QA system 100 that may beanswered by the content in the corpus of data 106. In one embodiment,the questions may be formed using natural language. The QA system 100may interpret the question and provide a response to the QA system user,e.g., QA system user 110, containing one or more answers to thequestion. In some embodiments, the QA system 100 may provide a responseto users in a ranked list of candidate answers.

The QA system 100 implements a QA system pipeline 108 which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system may receive aninput question which it then parses to extract the major features of thequestion, that in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms may look at the matching of terms and synonymswithin the language of the input question and the found portions of thecorpus of data. Other reasoning algorithms may look at temporal orspatial features in the language, while others may evaluate the sourceof the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the Watson™ QA system has regarding the evidence that the potentialresponse, i.e. candidate answer, is inferred by the question. Thisprocess may be repeated for each of the candidate answers until theWatson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

It should be appreciated that, while the Watson™ QA system is used asone example of a QA system that may be used with some illustrativeembodiments, the illustrative embodiments, and the present invention asa whole, are not limited to implementation in, or in association with,the Watson™ QA system. To the contrary, and natural language processingbased question answering system may be augmented, in view of the presentdescription, to implement the mechanisms of the illustrative embodimentswithout departing from the spirit and scope of the present invention.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 may be connected toNB/MCH 202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 may be connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Putin's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline300, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Putin” may be identified as aproper name of a person with which the question is associated, “closest”may be identified as a word indicative of proximity or relationship, and“advisors” may be indicative of a noun or other language topic.

The identified major features may then be used during the questiondecomposition stage 330 to decompose the question into one or morequeries that may be applied to the corpus of data/information 345 inorder to generate one or more hypotheses. The queries may be applicableto both structured and non-structured data in the corpus ofdata/information 345 and may be generated in any known or laterdeveloped query language, such as the Structure Query Language (SQL),NoSQL, full text searches, or the like. The queries may be applied toone or more databases storing information about the electronic texts,documents, articles, websites, and the like, that make up the corpus ofdata/information 345. That is, these various sources themselves,collections of sources, and the like, may represent different corpora347 within the corpus 345. There may be different corpora 347 definedfor different collections of documents based on various criteriadepending upon the particular implementation. For example, differentcorpora may be established for different topics, subject mattercategories, sources of information, or the like. As one example, a firstcorpora may be associated with healthcare documents while a secondcorpora may be associated with financial documents. Alternatively, onecorpora may be documents published by the U.S. Department of Energywhile another corpora may be IBM Redbooks documents. Any collection ofcontent having some similar attribute may be considered to be a corpora347 within the corpus 345.

The queries may be applied to one or more databases, knowledge bases,indices, or the like, that store information about the electronic texts,documents, articles, websites, and the like, that make up the corpus ofdata/information, e.g., the corpus of data 106 in FIG. 1. The queriesbeing applied to the corpus of data/information 345 at the hypothesisgeneration stage 340 to generate results identifying potentialhypotheses for answering the input question which can be evaluated. Thatis, the application of the queries results in the extraction of portionsof the corpus of data/information 345 matching the criteria of theparticular query. These portions of the corpus may then be analyzed andused, during the hypothesis generation stage 340, to generate hypothesesfor answering the input question. These hypotheses are also referred toherein as “candidate answers” for the input question. For any inputquestion, at this stage 340, there may be hundreds of hypotheses orcandidate answers generated that may need to be evaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e. a measure of confidence in thehypothesis.

In the synthesis stage 360, the large number of relevance scoresgenerated by the various reasoning algorithms may be synthesized intoconfidence scores for the various hypotheses. This process may involveapplying weights to the various scores, where the weights have beendetermined through training of the statistical model employed by the QAsystem and/or dynamically updated, as described hereafter. The weightedscores may be processed in accordance with a statistical model generatedthrough training of the QA system that identifies a manner by whichthese scores may be combined to generate a confidence score or measurefor the individual hypotheses or candidate answers. This confidencescore or measure summarizes the level of confidence that the QA systemhas about the evidence that the candidate answer is inferred by theinput question, i.e. that the candidate answer is the correct answer forthe input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which may compare theconfidence scores and measures, compare them against predeterminedthresholds, or perform any other analysis on the confidence scores todetermine which hypotheses/candidate answers are the most likely to bethe answer to the input question. The hypotheses/candidate answers maybe ranked according to these comparisons to generate a ranked listing ofhypotheses/candidate answers (hereafter simply referred to as “candidateanswers”). From the ranked listing of candidate answers, at stage 380, afinal answer and confidence score, or final set of candidate answers andconfidence scores, may be generated and output to the submitter of theoriginal input question. The set of candidate answers is output via agraphical user interface generated using the mechanisms of theillustrative embodiment, which provide the user with the tools forcollaborating with the QA system to review, evaluate, and modify thelisting of candidate answers and the evidence associated with thesecandidate answers that is evaluated by the QA system.

As shown in FIG. 3, the illustrative embodiments provide question/answercaching logic 390 and QA cache 395 which may interface with the questionand topic analysis stage 320 and final answer and confidence stage 380.As discussed above, during the question and topic analysis stage 320,the input question 310 is parsed using natural language processing (NLP)techniques to extract major features from the input question, classifythe major features according to types, etc. The extracted features mayalso be provided to the question/answer caching logic 390. Thequestion/answer caching logic 390 may compare the extracted features tothose stored for previously processed questions in the QA cache 395. Thequestion/answer caching logic 390 may determine a degree of matchingbetween the extracted features for the current input question 310 andthe features of the previously processed questions in the QA cache 395.In some embodiments, the extracted features of the current inputquestion 310 may also be compared against features of the candidateanswers stored in the QA cache 395 for previously submitted questions.The degree of matching, or match value, may be compared against one ormore predetermined thresholds to determine if an entry in the QA cache395 corresponding to a previously processed question has sufficientsimilarity to the current input question 310. If not, then the currentinput question 310 is processed in a manner as previously describedabove and a new entry for the current input question 310 is created bythe question/answer caching logic 390 in the QA cache 395. The creationof the new entry may require the eviction of an existing entry if the QAcache 395 is presently full. Any cache eviction policy may beimplemented by the question/answer caching logic, including leastrecently used (LRU), least used (if counters are associated with cacheentries), or the like.

In some illustrative embodiments, rather than creating a new entry inthe QA cache 395, in some cases the content of the new entry may insteadbe appended as additional entry information for an already existingentry in the QA cache 395. That is, if the current input question 310 isdetermined to be sufficiently similar, such as by comparison of ameasure of a degree of matching with one or more thresholds, to anexisting cached question in the QA cache 395, but not similar enough toutilize the QA cache 395 entry as the source of the candidate answersfor the current input question 310, then the elements of the currentinput question 310 may be added to the existing QA cache 395 entry alongwith any candidate answer information generated by the normal operationof the QA system pipeline on the current input question, therebygradually expanding the applicability of the QA cache 395 entry tosufficiently similar subsequent questions.

In either case, the entry that is created in the QA cache 395 for thecurrent input question 310, or the updates to an existing QA cache 395entry, comprises the set of extracted features for the input question310. Ultimately, once the final answer, or final set of candidateanswers, is generated at final answer and confidence stage 380, thefinal answer and confidence information is also stored in the entry inthe QA cache 395. This may include, for example, information about thefinal answer(s) that were generated, the confidence measure(s)associated with the final answer(s), any rankings for the finalanswer(s) generated for generating a ranked listing, any evidenceinformation indicating the evidence used to generate the answer(s) andconfidence measure(s), or any other pertinent information for questionanswering used by the QA system. Thus, the entry in the QA cache 395comprises at least a set of extracted features from the input question310 and the final answer and confidence information for the one or moreanswers generated by the QA system pipeline 300. Other information aboutthe input question may also be stored in the entry, such as a determineddomain of the input question, information about a submitter of the inputquestion, time of receipt of the input question, or any othercharacteristics about the input question that may have a bearing on itsability to be used to provide answers for subsequently submitted inputquestions.

If the question/answer caching logic 390 searches the QA cache 395 for amatching entry having extracted features, candidate answer features, orthe like, for a previously processed question that sufficiently matchthe extracted features for the current input question 310, a matchingentry is found, then the question/answer caching logic 390 may forwardthe corresponding final answer and confidence information stored in thematching entry to the final answer and confidence stage 380 for outputas the final answer and confidence information for the current inputquestion 310, as illustrated by the communication connection 392. Inessence, the question/answer caching logic 390, via communicationconnection 392, circumvents stages 330-370 of the QA system pipeline 300and avoids the computation time and resources required to implementthese stages 330-370.

The determination as to whether an entry exists in the QA cache 395 thatsufficiently matches the input question 310, as mentioned above, may bebased on a degree of matching between the extracted features of theinput question 310 and the extracted features, candidate answerfeatures, and/or the like, stored in the various entries of the QA cache395. For example, the key terms, phrases, and the like, extracted fromthe input question, along with the corresponding type information, e.g.,whether the term is a focus of the question, identifies a lexical answertype (LAT), is part of a clue section, is a particular part of speech,or the like, may be compared to terms, phrases, etc., and typeinformation stored in the entries of the QA cache 395. Moreover, ifadditional information is being used in the QA cache 395 and the QAsystem 300, such as domain, question submitter information, time ofreceipt information, or other characteristics information, similarcomparisons may be made between this additional information extractedfrom the input question 310 and the stored information in the entries ofthe QA cache 395.

Quantitative values may be calculated for matching characteristicsbetween the input question and the entries of the QA cache 395. That is,characteristics that match between the input question 310 and an entryin the QA cache 395 increase a degree of matching score for the entry.Various weights may be applied to different types of characteristicinformation, e.g., greater weights given for matching domains, greaterweights for matching submitters of questions, giving greater weights formatching terms in candidate answers of entries in the QA cache 395, etc.Combining the quantitative values for the matching characteristics givesa measure of the degree of matching between the input question 310 andan entry in the QA cache 395. This degree of matching measure may becompared against one or more threshold values to determine if the entryis sufficiently matching of the input question 310 to warrant using theentry's corresponding answer and confidence measure information as thefinal answer and confidence information for the input question 310.

In one illustrative embodiment, the one or more threshold valuescomprises a minimum level of matching required for the entry to beconsidered a matching entry. If this minimum level of matching is met orexceeded by the degree of matching measure for the entry, then the entrymay be determined to be sufficiently matching of the input question. Ifthis minimum level of matching is not met or exceeded by the degree ofmatching measure for the entry, then the entry is determined to be notsufficiently matching of the input question. This generation of measuresof degrees of matching and comparison to one or more thresholds may berepeated for each of the entries in the QA cache 395.

Multiple entries in the QA cache 395 may be found to be sufficientlymatching of the input question 310. Each of the entries being found tobe sufficiently matching of the input question 310 may be returned tothe final answer and confidence stage 380 for use in generating thefinal answer and confidence information. Thus, multiple sets ofcandidate answers and corresponding confidence information may bereturned to the final answer and confidence stage 380 and may be mergedto generate a single final answer and confidence measure output, aranked listing of candidate answers and corresponding confidenceinformation, or the like.

In some illustrative embodiments, a plurality of thresholds may beutilized. For those entries whose degree of matching measures meetvarious ones of the thresholds, various operations may be implemented toreturn the corresponding answer and confidence information to asubmitter of the input question 310 in a manner that the submitter mayutilize the answer and confidence information in accordance with itslevel of matching. For example, if the degree of matching of an entry isabove a first threshold indicative of a possible matching entry, butless than a second threshold indicative of an actual matching entry,then the entry may be returned as a possible matching entry and itscorresponding answer and confidence information may be returned as apossible answer and confidence for the input question 310. User feedbackmay be returned to the final answer and confidence stage 380 logic inresponse, and this information may be used to determine whether theinput question 310 should be passed through the remaining stages 330-370to generate a more accurate answer and confidence for the input question310. That is, if the user determines, after being presented with thepossible answers and confidence measures, that none of the possibleanswers and confidence measures are sufficient, then the user may returnfeedback indicating so which is then provided to the question/answercaching logic 390 which causes the question and topic analysis stage 320logic to forward the question on to the later stages 330-370.

In addition, if a user selects a candidate answer and confidenceinformation as being a correct final answer for the input question 310,this information may be returned to the final answer and confidencestage 380 logic which communicates it to the question/answer cachinglogic 390. Such information may be used to modify the relative level ofmatching of the entry in the QA cache 395. For example, one or moreweights associated with the entry may be adjusted to give the entrygreater weighting when determining a degree of matching of the entrywith subsequent similar questions submitted to the QA system pipeline300.

Thus, the illustrative embodiments provide mechanisms for cachingquestions information and their corresponding answer and confidencemeasure information and using this information to answer subsequentlysubmitted questions. The mechanisms of the illustrative embodiments maycircumvent and avoid the computation time and resource utilizationrequired to perform a full analysis and answer of an input question.This greatly improves performance of the QA system and increases thespeed at which the QA system may answer similar questions.

For example, assume that a previously submitted question is of the type“Who was the first President of the United States?” and the candidateanswers that were returned are “President Washington” with a confidenceof 60%, “George Washington” with a confidence of 90%, and “Washington”with a confidence of 85%. The features extracted from this examplequestion may be “who”, “first”, “President”, and “United States.” Theextracted feature information and answer/confidence information may becached in the QA cache 395 in the manner previously described above.

Assume that a subsequent question is submitted to the QA system 300 ofthe type “Was George Washington the first President?” The featuresextracted from this question may be “Was”, “George Washington”, “first”,and “President.” From this example, it can be determined that theterms/phrases “first” and “President” match between the entry in the QAcache 395 and the extracted feature of the subsequently submittedquestion, both with regard to the content of these terms/phrases as wellas additional deep semantic analysis of the use of these terms/phrasesin the context of the questions, whereas the terms “was” and “GeorgeWashington” do not have a match in the extracted features of the entryin the QA cache 395. However, the term “George Washington” does matchone of the candidate answers for the entry in the QA cache 395. Thus,after identifying the matching features of the subsequent question withthe features stored in the entry in the QA cache 395, and calculating ascore based on the identified matching, the score may be comparedagainst a threshold. If the threshold is met or exceeded, then adetermination is made that the entry matches the input question and thecorresponding candidate answers may be returned as answers for thesubsequent question. In this case, if the previous entry is determinedto be matching, and it is determined that the subsequent question islooking for a “yes” or “no” answer, and the candidate answers comprisethe answer “George Washington”, then an answer of “yes” may be returnedwith a high confidence rating.

FIG. 4 is a flowchart outlining an example operation of a QA systempipeline implementing caching logic in accordance with one illustrativeembodiment. As shown in FIG. 4, the operation starts by receiving aninput question for processing by the QA system (step 410). The inputquestion is parsed and analyzed to extract features from the inputquestion (step 420). The extracted features of the input question areprovided to question/answer caching logic that performs a search of a QAcache for entries having matching features/candidate answers to that ofthe extracted features of the input question (step 430). A determinationis made as to whether a matching entry is found in the QA cache (step440). If so, the candidate answer and confidence information stored inthe matching entry is returned as answer and confidence information forthe input question (step 450). If not, then a new entry is generated inthe QA cache using the extracted features of the input question (step460). The input question is processed by the QA system pipeline (step470) and the resulting candidate answer and confidence information isstored in the new entry in the QA cache (step 480). The operation thenterminates.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method, in a data processing system comprising a processor and amemory, for answering an input question, the method comprising:receiving, in the data processing system, an input question to beanswered from a source; processing, by the data processing system, theinput question to extract one or more features of the input question;comparing, by the data processing system, the extracted one or morefeatures to cached features stored in one or more entries of a questionand answer (QA) cache of the data processing system; determining, by thedata processing system, whether there is a matching entry in the one ormore entries of the QA cache based on results of the comparing;retrieving, by the data processing system, in response to a matchingentry being present in the one or more entries of the QA cache,candidate answer information from the matching entry; and returning, bythe data processing system, the retrieved candidate answer informationto the source of the input question as candidate answer information foranswering the input question.
 2. The method of claim 1, wherein, inresponse to determining that there is a matching entry in the one ormore entries of the QA cache, processing of the input question by apredetermined set of QA system pipeline stages is circumvented.
 3. Themethod of claim 1, further comprising: in response to determining thatthere is not a matching entry in the one or more entries of the QAcache, processing the input question through a QA system pipeline togenerate one or more generated candidate answers for answering the inputquestion.
 4. The method of claim 3, further comprising: in response todetermining that there is not a matching entry in the one or moreentries of the QA cache, generating a new cache entry in the QA cachefor the input question, wherein the new entry in the QA cache comprisesthe extracted one or more features of the input question stored inassociation with the one or more generated candidate answers for theinput question.
 5. The method of claim 3, further comprising: inresponse to determining that there is not a matching entry in the one ormore entries of the QA cache, and determining that a selected entry inthe QA cache has a predetermined degree of similarity to the inputquestion, updating the selected entry with the extracted one or morefeatures of the input question in association with the one or moregenerated candidate answers for the input question.
 6. The method ofclaim 1, wherein, in response to determining that there is not amatching entry in the one or more entries of the QA cache: determiningif there is a subset of entries in the one or more entries that issimilar to the input question; and outputting a listing of the subset ofentries to a user for user selection of an entry in the subset ofentries to be retrieved and used to generate candidate answers for theinput question.
 7. The method of claim 1, wherein determining whetherthere is a matching entry in the one or more entries of the QA cachecomprises: generating, for each entry in the QA cache, a match valueindicative of a degree of matching between the one or more extractedfeatures of the input question to cached features of the entry in the QAcache; and comparing the match value to one or more threshold valuesindicating one or more requisite degrees of similarity between the inputquestion and an entry in the QA cache.
 8. The method of claim 7,wherein: in response to the match value equaling or exceeding a firstthreshold value, a corresponding entry is determined to match the inputquestion, and in response to the match value being less than the firstthreshold value, but the match value being equal to or greater than asecond threshold value, determining that the corresponding entry issufficiently similar for updating the corresponding entry with the oneor more extracted features of the input question.
 9. The method of claim1, wherein entries in the QA cache comprise extracted features forpreviously processed questions with corresponding candidate answerinformation and confidence measures associated with the candidateanswers.
 10. The method of claim 9, wherein the entries in the QA cachefurther comprise at least one of ranking information for the candidateanswer, evidence information indicating the evidence used to generatethe candidate answer, a determined domain of a corresponding previouslyprocessed question, information about a submitter of the correspondingpreviously processed question, or time of receipt of the correspondingpreviously processed question. 11-20. (canceled)